The researchers analysed cross-sectional data from 2,389 Qatari Biobank participants, split almost perfectly evenly between 1,192 males and 1,197 females. Rather than examining individual measurements in isolation, the team constructed five composite scores from standardised z-scores, each capturing a distinct health domain: muscular strength, blood pressure and adiposity, respiratory function, cognitive function, and a socio-environmental index combining education, income, tobacco exposure and night-shift work. This composite approach matters because single measurements can be noisy and domain-specific patterns can hide inside averages. By aggregating related variables into coherent phenotypes, the analysis could ask whether men and women differ across whole physiological systems rather than on isolated laboratory values.

The headline statistical result was unambiguous. A multivariate analysis of variance, which tests whether the entire profile of five scores differs between the sexes simultaneously, produced a Pillai’s Trace of 0.598 with F(5,667) = 198.5 and a p-value below 0.001. In practical terms, sex explained an enormous share of the variation across these combined health domains. But the study’s real contribution lies in what happened next, when the researchers decomposed that overall signal domain by domain, estimated effect sizes using Cohen’s d, and then re-estimated everything in multivariable models adjusting for age, education, income and smoking. The picture that emerged was one of starkly domain-specific differences, some of which are biology and some of which are policy targets.

The largest difference anywhere in the dataset was muscular strength, where males scored dramatically higher, with an effect size of d = 2.41 in the raw comparison. Even after adjustment for age, education, income and smoking, the gap remained at d = 2.28, an effect size so large it sits at the boundary of what social and behavioural science considers practically maximal. The authors interpret this, correctly, as consistent with established structural dimorphism: differences in muscle mass and body composition that are rooted in physiology, not in unequal access to resources or healthcare. Their conclusion is pointed and policy-relevant. Such differences should not be interpreted as disparities requiring intervention, and treating them as such would misdirect resources away from the gaps that are genuinely actionable.

The respiratory findings deliver the study’s most instructive methodological lesson. When lung function was measured as raw volumes, such as forced expiratory volume in one second and forced vital capacity, males showed a massive advantage of d = 2.05, seemingly comparable in magnitude to the strength difference. But the team had prespecified a critical alternative: respiratory function was also quantified using reference-equation z-scores, which standardise each individual’s lung volumes against expected values for their age, sex, height and ethnicity. Under this properly referenced metric, the male advantage collapsed from d = 2.05 to d = 0.30, and after multivariable adjustment it shrank further to d = 0.17. In other words, the enormous raw-volume gap was almost entirely an artefact of body size. Men have bigger lungs because they have bigger bodies, not because their respiratory health is better. Any surveillance system that tracks raw spirometry volumes across sexes without referencing would systematically misread anatomy as pathology.

The cardiometabolic domain tells the opposite story, and it is here that the study’s clearest warning for prevention policy emerges. Females showed a more favourable blood pressure and adiposity profile, with an effect size of d = -0.58 in the raw comparison that actually strengthened to d = -0.82 after adjustment. On its face, that looks like good news for women. But the researchers then tested whether this female advantage was uniform across the lifespan, and it was not. Below age 50, the female cardiometabolic advantage was d = -0.78, whereas at age 50 and above it narrowed to d = -0.37, a sex-by-age interaction with a p-value below 0.001. The pattern is consistent with a menopause-related narrowing: the well-documented cardiometabolic protection that women carry through their reproductive years attenuates after midlife, and in this Qatari cohort it attenuates substantially.

That finding has direct implications for how screening and prevention are timed in Gulf populations. If the female advantage in blood pressure and adiposity erodes after 50, then the midlife transition marks the point at which Qatari women’s cardiovascular risk profile begins converging toward that of men, and prevention programmes that assume women remain relatively protected deep into later life may miss the window in which intervention is most effective. The authors flag this menopause-related narrowing, together with the divergent exposure profiles discussed below, as the two findings with the clearest implications for population health policy. It is a reminder that sex differences in health are not static properties but trajectories that change across the life course, and that cross-sectional snapshots can conceal those dynamics unless age is explicitly modelled, as it was here through prespecified effect-modification analyses.

Cognitive function, by contrast, turned out to be largely a story about confounding. In the unadjusted comparison, males showed a small advantage on the cognitive composite, which drew on the Cambridge Neuropsychological Test Automated Battery. But after adjustment for age, education, income and smoking, the difference shrank to d = 0.18 with a p-value of 0.068, no longer statistically distinguishable from zero. The team also ran prespecified sensitivity analyses addressing selection into the cognitive subsample, component overlap between the composites, and internal consistency, which strengthens confidence that these attenuations reflect genuine statistical structure rather than analytical fragility. The lesson echoes the respiratory finding: apparent sex differences in a health domain can dissolve once socioeconomic and behavioural context is accounted for, which is precisely why the study distinguishes so carefully between dimorphism and disparity.

Perhaps the most socially revealing result concerns the socio-environmental composite, which showed essentially no net sex difference, d = -0.01, but only because two opposing gradients cancelled each other out. Males in the cohort had higher education and income, advantages that would push their composite score upward, but they also carried substantially greater exposure to tobacco and to night-shift work, risk factors that pushed it downward. Averaged together, men and women looked environmentally identical. Looked at component by component, they occupy strikingly different risk landscapes. For policy, this cancellation is itself the finding: a summary index that reports no sex gap would conceal the fact that Qatari men disproportionately bear the burden of smoking and shift-work exposure, both established drivers of cardiometabolic and respiratory disease, while women’s environmental profile differs in composition rather than magnitude. Targeted interventions, the authors argue, need to see these opposing gradients, not the net zero.

The study’s overall conclusion is that sex-specific health phenotypes in this Qatari cohort are domain-specific, and that only some of the observed differences represent actionable health inequity. Muscular strength gaps and raw lung-volume differences reflect normative physiological dimorphism and should be excluded from disparity metrics. The narrowing of women’s cardiometabolic advantage after midlife and the divergent socio-environmental exposures of men and women are the signals that prevention policy should act on. The authors are appropriately cautious about generalisability, noting that whether these patterns extend to other Middle Eastern populations requires comparative data, and the cross-sectional design cannot establish causal sequences within individuals. Still, for a region where sex-specific differences have often been overlooked in population health strategy, the analysis offers a template: measure domains comprehensively, reference measurements properly, adjust for social context, model age explicitly, and only then decide which differences between men and women are biology to be understood and which are inequities to be fixed.

Subject of Research: Sex-specific health phenotypes and their policy implications in a Qatari population cohort

Article Title: Sex-specific health phenotypes in a large qatari cohort: a cross-sectional analysis with implications for population health policy

Article References: Sex-specific health phenotypes in a large qatari cohort: a cross-sectional analysis with implications for population health policy. (n.d.). https://doi.org/10.1186/s12889-026-29801-z

Image Credits: AI Generated

DOI: 10.1186/s12889-026-29801-z

Keywords: sex differences, sexual dimorphism, Qatar Biobank, health phenotypes, cardiometabolic risk, respiratory function, muscular strength, menopause, population health policy, cross-sectional study, tobacco exposure, cognitive function

The study, titled “Parent-Reported Household Tobacco and E-cigarette Exposure and Asthma Control in Children: A Cross-Sectional Study,” drew on electronic health record data from a large, diverse urban pediatric primary care network affiliated with Children’s Hospital of Philadelphia. The research team examined encounters recorded between July 2023 and March 2026, a 33-month window in which parents of children aged one year or older with an asthma diagnosis completed two standardized instruments: a parent tobacco treatment platform that asked who in the family used combustible tobacco or e-cigarettes, and an asthma control tool that assessed symptoms, medication use, and healthcare utilization. By pairing these two data sources across tens of thousands of visits, the investigators were able to examine how different patterns of household nicotine use tracked with day-to-day asthma outcomes as reported by the people who know the children best.

The scale of the dataset is one of its distinguishing strengths. Among 48,118 eligible encounters, 48 percent involved children aged six to twelve years, 42 percent were female, 34 percent were Non-Hispanic Black, 43 percent were covered by Medicaid, and 30 percent came from neighborhoods rated as having very low opportunity by the Child Opportunity Index. Household use of both tobacco and e-cigarettes was reported in 5 percent of visits, or 2,500 encounters; tobacco-only use in 9 percent, or 4,238 encounters; and e-cigarette-only use in 3 percent, or 1,427 encounters. This diversity matters, because asthma burden and tobacco exposure are not evenly distributed across pediatric populations, and the study’s statistical adjustments were designed to account for exactly these sociodemographic differences.

To isolate the effect of household exposure from other factors that influence asthma control, the researchers used multivariable logistic regression, estimating adjusted odds ratios with 95 percent confidence intervals while controlling for child age, sex, insurance status, and the Child Opportunity Index of the child’s neighborhood. The Children’s Hospital of Philadelphia institutional review board deemed the study exempt under the Common Rule. The statistical approach allowed the team to ask a deceptively simple question: after accounting for a family’s circumstances, does the presence of tobacco or e-cigarette use in the home still predict poorer asthma control? The answer, across multiple outcome domains, was yes.

Children exposed to both combustible tobacco and e-cigarettes in the home showed the broadest and strongest associations with poor asthma outcomes. Combined exposure was linked to more activity-related symptoms, with an adjusted odds ratio of 1.3; more nighttime symptoms, also 1.3; greater albuterol or rescue inhaler use, at 1.2; worse parent ratings of asthma control, at 1.3; lower parental comfort with asthma management, at 1.3; and hospital admissions in the past year, at 1.2. Children with combined exposure were also more likely to have poorly controlled asthma status, at 1.1, and were substantially more likely to have uncontrolled asthma, with an adjusted odds ratio of 1.4. In practical terms, this means more flares, more disrupted sleep, more missed activity, and more hospital stays for the children breathing air shared with the heaviest household nicotine users.

The most striking result, however, concerned e-cigarettes alone. E-cigarette-only exposure was independently associated with more asthma flares, with an adjusted odds ratio of 1.3, and more activity-related symptoms, at 1.4 — figures comparable to or exceeding those seen with combined tobacco and e-cigarette exposure. Tobacco-only exposure showed more modest but still significant associations across multiple domains. Not every measure moved in the same way: parent-reported emergency department or urgent care utilization and oral steroid use were not significantly associated with any exposure type after adjustment. But the pattern across flares, symptoms, and hospitalizations paints a consistent picture that vapor is not benign for asthmatic airways.

“There is no safe level of tobacco or e-cigarette exposure when it comes to children. Parents and caregivers who use any tobacco or nicotine products should make every effort to quit, using evidence-based options like varenicline, nicotine replacement therapy, and counseling,” said Brian Jenssen, MD, MSHP, FAAP, associate professor of pediatrics at Children’s Hospital of Philadelphia and the study’s principal investigator. His message to families is blunt: the long-promoted idea that vaping indoors is a harm-reduction strategy for households with children does not survive contact with the data on pediatric asthma.

“Many parents believe e-cigarettes are a safer alternative to cigarettes, both for themselves and for their children’s health. Our findings suggest that assumption doesn’t fully hold up for kids with asthma,” Jenssen said. He also framed the results as a call to action for clinicians: “It reinforces the need for pediatric care teams to screen for all forms of household nicotine and tobacco use — not just cigarettes — and to offer cessation support for both, as part of routine asthma care.” In other words, the clinic visit for an asthma flare may itself be the most teachable moment to ask not only whether anyone in the home smokes cigarettes, but whether anyone vapes — and to have cessation resources ready for either answer.

The biological plausibility behind these associations is well established in the broader literature. Secondhand smoke worsens respiratory illnesses in children, and asthma is particularly sensitive to inhaled irritants, which provoke airway inflammation, mucus production, and bronchial hyperreactivity. E-cigarette aerosols contain nicotine, ultrafine particles, flavoring compounds, and thermal degradation products, several of which are known to irritate or damage respiratory epithelium. What has been lacking until now is large-scale, real-world evidence on how e-cigarette exposure alone affects asthma control in children — a gap this study helps fill by showing that vapor-only households still carry measurable risk for the asthmatic children living in them.

The authors are careful to note the study’s design: it is cross-sectional, capturing exposure and outcomes at the same point in time, so it demonstrates associations rather than proving causation, and it relies on parent-reported exposure and asthma control rather than objective biomarkers or clinical measures. Still, the sheer number of encounters, the diversity of the population, and the consistency of the adjusted associations across symptom, medication, and hospitalization domains give the findings considerable weight. The work was funded by the National Institutes of Health under grant R37-CA282153, with Jenssen as principal investigator. Jenssen is scheduled to present the research at 3:40 p.m. PT on Saturday, October 3, at the Marriott Marquis, for the conference’s Section on Nicotine and Tobacco Prevention and Treatment, and will participate in a press Soundbite Session on Sunday, October 4. For the estimated six million American children with asthma — and the many households where a parent vapes believing it protects their kids — the study’s core conclusion lands with force: when a child’s lungs are already inflamed, the air they breathe at home matters, whatever form the nicotine takes.

Subject of Research: Household tobacco and e-cigarette exposure and asthma control in children

Article Title: E-cigarettes, tobacco in-home exposure linked to children’s asthma flare-ups

Article References: E-cigarettes, tobacco in-home exposure linked to children’s asthma flare-ups. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: e-cigarettes, secondhand smoke, pediatric asthma, asthma control, tobacco exposure, nicotine, Children's Hospital of Philadelphia, American Academy of Pediatrics, public health, smoking cessation, respiratory health, cross-sectional study

The study, published in Holistic Integrative Oncology, drew on five cycles of the National Health and Nutrition Examination Survey, or NHANES, the rolling federal program that collects standardized laboratory measurements from a cross-section of the civilian, noninstitutionalized US population. The researchers assembled a final analytic population of 7,174 men with concurrent measurements of serum cotinine, total PSA, and free PSA. The free-to-total PSA ratio, a value clinicians use to sharpen the interpretation of borderline PSA results, was calculated directly from the two measured fractions. Cotinine, measured with isotope dilution high-performance liquid chromatography coupled to atmospheric pressure chemical ionization tandem mass spectrometry, served as the objective window onto recent nicotine exposure — capturing not only active smoking but also secondhand smoke and other nicotine sources that self-reported questionnaires routinely miss or misclassify.

What the analysis found was a story of two biomarkers going their separate ways. In fully adjusted multivariable linear regression models that accounted for age, race and ethnicity, body mass index, education, marital status, estimated kidney function, alcohol use, family income, and survey cycle, serum cotinine showed essentially no independent association with total PSA — the regression coefficient hovered at effectively zero, with a P value of 0.801. Yet the same exposure variable was consistently and significantly associated with a lower free-to-total PSA ratio: each additional nanogram per milliliter of cotinine corresponded to a decrease of roughly 0.005 percentage points in the ratio (95% confidence interval -0.00705 to -0.00283; P = 0.000005). The signal survived every tier of statistical adjustment, growing rather than shrinking as confounders were layered in, and the direction remained consistent when cotinine was log-transformed to temper the influence of heavy smokers.

The discordance between the two endpoints is what makes the result scientifically interesting. Total PSA reflects the overall circulating concentration of a kallikrein-related serine protease secreted by the androgen-responsive prostate gland, a concentration that climbs with benign prostatic hyperplasia, prostatitis, and prostate cancer alike. The free-to-total ratio, by contrast, depends not on how much PSA is in the blood but on how it is distributed between molecular forms — free PSA floating unbound versus PSA complexed with proteins such as alpha-1-antichymotrypsin. Because the two endpoints track different aspects of PSA chemistry, a compound that shifts the composition of circulating PSA without changing its total concentration would produce exactly the pattern observed: a null association with total PSA and a robust inverse association with the ratio.

Recognizing that a single cotinine measurement captures only a narrow biological window — days, not decades — the team added a second exposure dimension. From self-reported cigarette histories, they calculated pack-years, the classic epidemiological measure of cumulative smoking burden, for 6,132 of the participants, 3,433 of whom were current or former smokers. Correlation analyses confirmed that cotinine and pack-years point in the same direction but are far from interchangeable: the Pearson correlation between raw values was a modest 0.209, the Spearman rank correlation 0.270, and only after log-transforming both variables did the relationship strengthen to 0.337. A heavy lifetime smoker who quit years ago can carry low cotinine today; a recent relapser can carry high cotinine on a thin lifetime foundation. The two metrics measure genuinely different things.

That difference mattered in the sensitivity models. When pack-years replaced cotinine as the exposure variable in fully adjusted regressions, the association with PSA ratio vanished entirely (β = -0.00750, 95% CI -0.02010 to 0.00509; P = 0.243), and the log-transformed version of pack-years fared no better. The cotinine signal, in other words, cannot be explained away as a shadow of lifetime cigarette burden. One provocative secondary pattern emerged for total PSA: log-transformed pack-years showed a statistically significant inverse association in sensitivity analyses, hinting that decades of cumulative exposure may relate to total PSA through chronic tissue, vascular, or endocrine remodeling in ways that an acute biomarker cannot capture. The authors are careful to stress that nothing in these data should be read as evidence that smoking protects the prostate — tobacco exposure has established harmful effects across organ systems and is associated with worse outcomes after prostate cancer diagnosis, including higher mortality and progression risk.

What might cotinine actually be doing to PSA chemistry? The mechanistic clues point toward nicotinic acetylcholine receptor signaling and androgen biology. Cotinine, which persists in serum far longer than nicotine, binds cell-surface cholinergic alpha5 nicotinic receptors, which experimental studies have shown are upregulated in prostate cancer and drive tumor cell proliferation and invasion. Experimental work also suggests cotinine can interact with the androgen receptor and suppress its expression in animal prostate tissue. Because PSA secretion is androgen-regulated, cotinine-related modulation of androgen receptor signaling could preferentially reduce the epithelial secretion of free PSA, shifting a greater share of immunoreactive PSA toward the alpha-1-antichymotrypsin-complexed fraction — lowering the ratio while leaving total PSA nearly untouched. Tobacco-driven systemic inflammation could contribute as well by altering the acute-phase protein milieu in which PSA complexes circulate. These mechanisms remain inferential in a cross-sectional dataset, but they explain the observed geometry of the results with unusual precision.

The interpretation demands caution at every turn. NHANES participants were not enrolled because of suspected prostate disease, and no biopsy adjudication exists for this cohort, so the findings speak to biomarker-level variation in a general population, not to cancer incidence, diagnostic thresholds, or clinical decision-making. Serum cotinine is also shaped by CYP2A6-mediated metabolism, which varies across racial and genetic backgrounds, meaning identical cotinine values may reflect different actual nicotine intake. The authors noted that the cotinine-PSA ratio association appeared more pronounced in certain subgroups, including non-Hispanic White and non-Hispanic Black participants, men aged 40 to 49 and 70 to 79, and those with a body mass index above 18.5, but stratified patterns in cross-sectional data carry their own fragility. Smokers may also differ systematically in screening behavior, comorbidity, and healthcare access, and despite the standardized survey protocol, differential participation and missing laboratory data cannot be excluded as sources of selection.

Within those constraints, the study stakes out a genuinely novel position: it is, according to the authors, the first population-based analysis to link cotinine to prostate-related endpoints in a nationally representative sample. Its strength lies in the objective laboratory measurement of exposure, the large sample, and the statistical persistence of the PSA ratio association after extensive adjustment. Its limitation is time — a single cotinine measurement, a single PSA snapshot, and no way to order cause and effect. The authors call for longitudinal studies with repeated cotinine measurements, validated cumulative smoking metrics, nicotine metabolite profiling that includes trans-3′-hydroxycotinine, and adjudicated prostate disease outcomes to determine whether recent exposure status, cotinine metabolism, or something else entirely drives the association.

For clinicians and epidemiologists alike, the practical message is narrower than the biological one. Nothing in this analysis justifies changing PSA screening practice or interpreting an individual patient’s PSA ratio through the lens of a cotinine blood level. But the results do suggest that tobacco-related biological status is a real and quantifiable source of variation in one of the most widely used prostate biomarkers in medicine — a reminder that the numbers generated by screening assays are not static properties of a gland but dynamic readouts that absorb the chemical history of the person they come from. As molecular epidemiology continues to separate the recent exposure a biomarker captures from the cumulative burden a questionnaire recalls, studies like this one map the fault lines where those two measures diverge, and where the biology of tobacco meets the biochemistry of cancer screening.

Subject of Research: The association between serum cotinine, a biomarker of tobacco exposure, and prostate-related clinical endpoints including PSA measures

Article Title: Association between serum cotinine levels and prostate-related clinical endpoints

Article References: Wang, Z., Ge, Q., Anwaier, A., Xu, W., & Ye, D. (2026). Association between serum cotinine levels and prostate-related clinical endpoints. Holistic Integrative Oncology, 5(1), Article 76. https://doi.org/10.1007/s44178-026-00292-7

Image Credits: AI Generated

DOI: 10.1007/s44178-026-00292-7

Keywords: serum cotinine, prostate-specific antigen, free-to-total PSA ratio, tobacco exposure, NHANES, prostate cancer, pack-years, nicotine metabolism, prostate biomarkers, epidemiology, urology, cancer screening